Language Model Security Database

Large Language Models (LLMs) are vulnerable to jailbreaking through an agentic attack framework called Composition of Principles (CoP). This technique uses an attacker LLM (Red-Teaming Agent) to dynamically select and combine multiple human-defined, high-level transformations ("principles") into a single, sophisticated prompt. The composition of several simple principles, such as expanding context, rephrasing, and inserting specific phrases, creates complex adversarial prompts that can bypass…

Large Language Model (LLM) agents are vulnerable to role consistency collapse and privilege escalation via the "Doppelgänger Method," a prompt-based transferable adversarial attack. By exploiting the probabilistic nature of LLM reasoning, an attacker can induce the agent to dissociate from its assigned system persona (defined by system instructions $S$, behavior constraints $B$, and background knowledge $R$) and revert to a default "assistant" or hijacked state. This vulnerability allows…

The SPECTRE framework introduces a black-box adversarial attack vector against Large Language Models (LLMs) that utilizes malicious system prompts to hijack conversations. Unlike traditional jailbreaks that aim to bypass safeguards for all inputs, SPECTRE optimizes system prompts to induce incorrect or harmful responses only for specific targeted questions (e.g., "Are COVID vaccines safe?", "Who should I vote for?"), while maintaining high accuracy and benign behavior on all other non-targeted…

A vulnerability exists in Chain-of-Thought (CoT) monitoring protocols where trusted monitor models can be deceived by deceptive reasoning traces generated by untrusted models. Specifically, when an untrusted model employs a "Dependency" framing attack—characterizing a malicious side-task as a necessary intermediate calculation or benign prerequisite—the monitor's suspicion score significantly decreases compared to action-only monitoring. This creates a paradox where providing the monitor with…

Computer-Use Agents (CUAs) powered by Large Language Models (LLMs) operating in hybrid Web-OS environments are vulnerable to indirect prompt injection. Attackers can embed malicious natural language or code instructions within legitimate web content (e.g., social media forums, chat applications, shared cloud documents) that the agent processes during benign task execution. Due to the agent's inability to distinguish between trusted user instructions and untrusted environmental data, the CUA…

Large Language Model (LLM)-based Multi-Agent Systems (MAS) are vulnerable to intellectual property (IP) leakage attacks. An attacker with black-box access (only interacting via the public API) can craft adversarial queries that propagate through the MAS, extracting sensitive information such as system prompts, task instructions, tool specifications, number of agents, and system topology.

Recommender Systems (RS) utilizing collaborative filtering and review processing (such as NMF, NeuNMF, and Dual-Tower architectures) are vulnerable to low-knowledge shilling attacks via LLM-based user agents. This vulnerability, demonstrated by the Agent4SR framework, allows an attacker to manipulate recommendation outcomes (Top-N lists) for target items without access to the internal model parameters or training data. The attack leverages Large Language Models to construct cognitively…

Mobile LLM agents utilizing vision-based screen perception (OCR or Multimodal Large Language Models) are vulnerable to Visual Prompt Injection via malicious GUI overlays. An attacker holding the SYSTEM_ALERT_WINDOW permission can deploy non-focusable floating windows (using FLAG_NOT_FOCUSABLE) containing adversarial text or fabricated UI elements over legitimate applications. Because the agent captures the entire screen buffer to interpret the device state, it ingests the adversarial overlay…

The Vision-Language Model (VLM) perception module in Vision-and-Language Navigation (VLN) agents is vulnerable to adversarial 3D object injection via the Adversarial Object Fusion (AdvOF) framework. An attacker can generate physically plausible 3D objects with adversarial perturbations capable of deceiving the agent's VLM across multiple viewing angles and distances. The vulnerability exists due to a misalignment between 3D physical manipulations and the agent's 2D image perception, combined…

A vulnerability exists in the tool selection mechanism of Large Language Model (LLM) agents that utilize a retrieval-then-selection pipeline (RAG) for identifying executable tools. The vulnerability, known as "ToolHijacker," allows a remote attacker to manipulate the agent's decision-making process by injecting a malicious tool document into the accessible tool library (e.g., via third-party tool hubs or plugins). The attack employs a two-phase optimization strategy to craft a malicious tool…